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Chapter 5

PSYB01H3 Chapter Notes - Chapter 5: Simple Random Sample, Stratified Sampling, Nth Metal


Department
Psychology
Course Code
PSYB01H3
Professor
David Nussbaum
Chapter
5

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PSYB01 Chapter 5 – Sampling and Survey Research
Selecting Research Participants
- Sampling: the selection of individuals or other entities to represent a larger population
- Census: to study the entire population of interest
- Better to survey a limited number so there are more resources for follow-up
procedures
Sample Planning
- Define the Population
oStudents, disabled persons, elderly and adult samples report similar levels of
happiness
oWhen Dieners analyzed surveys of happiness in countries around the world, the
average level of happiness varied markedly (satisfaction with life domains
varies across cultures)
oCross-population generalizability: compare results obtained from samples of
different populations
- Define Sample Components
oElements: elementary units, individual member of population whose
characteristics are to be measured
oSampling frame: List of all elements in a population
oPopulation: Entire set of individuals or other entities to study
oRepresentative sample: “looks like” population from which it was selected (in
unrepresentative sample, some characteristics are overrepresented or
underrepresented)
oSample generalizability depends on the amount of sampling error (difference
between sample and population)
oEstimating Sampling Error
Inferential Statistics: tool for calculating sampling error
Sampling distributions for many statistics have a normal shape
Random Sampling Error: Variation owing purely to chance
Sample Statistic: value of a statistic such as a mean, computed from
sample data

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PSYB01 Chapter 5 – Sampling and Survey Research
Population Parameter: Value of a statistic computed using the data for
an entire population. Sample statistic is an estimate of a population
parameter
Sampling Methods
-Probability Sampling Methods: Probability of selection is known and is not zero
- Non-probability Sampling Methods: Sampling methods that do not let us know in
advance the likelihood of selecting each element
- Probability of Selection: likelihood that an element will be selected from the population
for inclusion in the sample
Probability Sampling Methods
- These methods have no systematic bias (nothing but chance determines which
elements are included in the sample)
- Four most common methods for drawing random samples
oSimple Random Sampling: procedure where cases are identified on basis of
chance
Random digit dialing: machine dials random numbers within the phone
prefixes
oSystematic Random Sampling: variant of simple random sampling
First element is selected randomly and then every nth element is
selected
Watch out for periodicity (sequence varies in some regular, periodic
pattern) (e.g. houses counted and the house on northwest side always
chosen because of sampling interval [number of cases from one
sampled case to another])
In that situation, starting point needs to be changed
oStratified Random Sampling: uses information known about population prior to
sampling to make sampling process more efficient
Ensures appropriate representation of elements across strata
Proportionate Stratified Sampling: ensures that sample is selected so
that the distribution of characteristics in the sample matches the
population
Disproportionate Stratified Sampling: Sampling where characteristics of
sample are disproportionate to population
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